Muhamad Risqi U. Saputra
Science Oxford, University of Oxford, Universitas Muhammadiyah Tangerang
Papers
7
Total Citations
281
H-Index
5
About
Muhamad Risqi U. Saputra is a leading researcher in robust state estimation and sensor fusion for mobile agents, specializing in the intersection of deep learning and robotics. His primary contributions lie in developing resilient localization systems that function in visually-denied environments—such as heavy smoke, darkness, or airborne particulates—where traditional optical methods fail. Saputra is best known for his work on **milliEgo** (130 citations), a pioneering system that fuses single-chip mmWave radar with deep sensor fusion to achieve accurate egomotion estimation, and **DeepTIO** (82 citations), a thermal-inertial odometry framework that uses “visual hallucination” to reconstruct visual features from thermal data. He further advanced the field with **graph-based thermal-inertial SLAM** (46 citations), integrating probabilistic neural networks to maintain robust mapping and localization under adverse visibility. Beyond robotics, Saputra has explored text-mining approaches to analyze trends in service robots and AI in tourism (2024). His work has been published in top venues like IEEE Transactions on Robotics and IROS, and his methods are critical for enabling autonomous navigation in challenging real-world conditions—from search-and-rescue to augmented reality.
Research Focus
Key Achievements
Top Papers
- 1milliEgo130 citations · 2020
- 2DeepTIO: A Deep Thermal-Inertial Odometry With Visual Hallucination82 citations · 2020
- 3Graph-Based Thermal–Inertial SLAM With Probabilistic Neural Networks46 citations · 2021
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- 6Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks3 citations · 2021
- 7DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination2 citations · 2019